Early Warnings for State Transitions

Authors

Caleb P. Roberts, University of Nebraska-LincolnFollow
Dirac Twidwell, University of Nebraska-LincolnFollow
Jessica L. Burnett, University of Nebraska-LincolnFollow
Victoria M. Donovan, University of Nebraska-LincolnFollow
Carissa L. Wonkka, University of Nebraska-LincolnFollow
Christine L. Bielski, University of Nebraska-LincolnFollow
Ahjond S. Garmestani, US Environmental Protection AgencyFollow
David G. Angeler, Swedish University of Agricultural SciencesFollow
Tarsha Eason, US Environmental Protection AgencyFollow
Brady W. Allred, University of MontanaFollow
Matthew O. Jones, University of MontanaFollow
David E. Naugle, University of MontanaFollow
Shana M. Sundstrom, University of Nebraska-LincolnFollow
Craig R. Allen, University of Nebraska-LincolnFollow

Document Type

Article

Date of this Version

11-2018

Citation

Roberts, C. P., Twidwell, D., Burnett, J. L., Donovan, V. M., Wonkka, C. L., Bielski, C. L., Garmestani, A. S., Angeler, D. G., Eason, T., Allred, B. W., Jones, M. O., Naugle, D. E., Sundstrom, S. M., and Allen, C. R. (2018). Early warnings for state transitions. Rangeland Ecology and Management 71(6): 659–670.

Comments

U.S. Government Work

Abstract

New concepts have emerged in theoretical ecology with the intent to quantify complexities in ecological change that are unaccounted for in state-and-transition models and to provide applied ecologists with statistical early warning metrics able to predict and prevent state transitions. With its rich history of furthering ecological theory and its robust and broad-scale monitoring frameworks, the rangeland discipline is poised to empirically assess these newly proposed ideas while also serving as early adopters of novel statistical metrics that provide advanced warning of a pending shift to an alternative ecological regime. We review multivariate early warning and regime shift detection metrics, identify situations where various metrics will be most useful for rangeland science, and then highlight known shortcomings. Our review of a suite of multivariate-based regime shift/early warning indicators provides a broad range of metrics applicable to a wide variety of data types or contexts, from situations where a great deal is known about the key system drivers and a regime shift is hypothesized a priori, to situations where the key drivers and the possibility of a regime shift are both unknown. These metrics can be used to answer ecological state-and-transition questions, inform policymakers, and provide quantitative decision-making tools for managers.